Biomolecular research is increasingly changing from phenomenological and descriptive to quantitative and predictive. The overall goal of the proposed research is to facilitate this paradigm shift in mechanistic studies of biomolecular recognition, which occurs at a wide range of spatial scales. Protein-ligand binding and allosteric regulation are the prototypical molecular recognition processes. Moving up the spatial scale, many intrinsically disordered proteins have now been identified, often involved in signaling or regulation by binding to their cellular targets. At the subcellular scale, exciting discoveries are being made about a membraneless form of micro-compartments, wherein specific proteins and RNAs are condensed but remain fluid. These intracellular bodies assemble reversibly in response to regulatory signals and can recognize bystander components for exclusion. High concentrations of bystander macromolecules (crowders) are always present in the cellular environments and affect all these molecular recognition processes. Irrespective of spatial scales, the fundamental basis of molecular recognition is the molecular physical properties, including molecular interactions and motions. To gain deep mechanistic knowledge on all these molecular recognition processes, the proposed research will use three complementary approaches. Theoretical models will be developed to test mechanistic hypotheses and guide experimental design and to establish the framework for relating thermodynamic and mechanistic properties to molecular physical properties. The framework will be implemented computationally, through molecular simulations and atomistic-level calculations. Experimental measurements will be made to obtain critical information, which will also serve to inspire theoretical models and validate computational results. Through the integration of the three approaches, the effects of macromolecular crowding will be characterized, such that the knowledge from dilute-solution studies can be transferred to the cellular context. The deep, quantitative understanding of biomolecular recognition to be achieved will enable accurate predictions of mechanistic properties and yield opportunities for drug design through altering mechanistic pathways.